Automatic facial action unit (AU) detection in videos isthe key ingredient to all systems that utilize a subjectface for either interaction or analysis purposes. Withthe ever growing range of possible applications,achieving a high accuracy in the simplest possible mannergains even more importance. In this paper, we present newfeatures obtained by applying local binary patterns toimages processed by morphological and bilateral filters.We use as features the variations of these patternsbetween the expressive and neutral faces, and show thatwe can gain a considerable amount of accuracy increase bysimply applying these fundamental image processing toolsand choosing the right way of representing the patterns.We also use these features in conjunction with additionalfeatures based on facial point geometrical relationsbetween frames and achieve detection rates higher thanmethods previously proposed, using a small number offeatures and basic support vector machine classification.